Journal ArticleDOI
Is Image-based CAPTCHA Secure Against Attacks Based on Machine Learning? An Experimental Study
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TLDR
The results show current image-based CAPTCHAs to deter automated scripts and malicious programs provide a false sense of security.About:
This article is published in Computers & Security.The article was published on 2020-01-01. It has received 27 citations till now. The article focuses on the topics: CAPTCHA.read more
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Adversary Models for Mobile Device Authentication
TL;DR: The proposed classification of adversaries provides a strong and practical adversary model that offers a comparable and transparent classification of security properties in mobile device authentication.
Journal ArticleDOI
A Password-Based Authentication System Based on the CAPTCHA AI Problem
TL;DR: This article presents a challenge-response password-based authentication system based on the Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) AI hard problem and proves that the system is probabilistic and very sensitive to changes in its parameters.
Journal ArticleDOI
An investigation of the usability of image-based CAPTCHAs using PROMETHEE-GAIA method
TL;DR: A comparative analysis of seven image-based CAPTCHAs based on three different criteria: time to find a solution, a number of attempts, and task difficulty suggested which CAPTCHA offered better human accuracy and lower machine attack rates compared to the existing approaches.
Journal ArticleDOI
Applying Visual Cryptography to Enhance Text Captchas
TL;DR: Using the features of the randomness for each encoding process in visual cryptography and the visual recognizability with naked human eyes, VC is applied to design and enhance text-based captcha (VCETC), and the recognition rate is in some degree decreased.
Journal ArticleDOI
A cloud endpoint coordinating CAPTCHA based on multi-view stacking ensemble
TL;DR: In this article, a novel cloud endpoint coordinating CAPTCHA based on multi-view stacking ensemble (MVSE) is proposed to make most use of the computing power of endpoint devices and reduce the calculation pressure of cloud system.
References
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Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
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Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Book
Data Mining: Practical Machine Learning Tools and Techniques
TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
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Efficient Estimation of Word Representations in Vector Space
TL;DR: This paper proposed two novel model architectures for computing continuous vector representations of words from very large data sets, and the quality of these representations is measured in a word similarity task and the results are compared to the previously best performing techniques based on different types of neural networks.